gen_etruncFUN

Produce function to compute expectation of truncated 
 error distribution from log cdf and log pdf (using numerical integration)

The R package 'ashr' implements an Empirical Bayes
approach for large-scale hypothesis testing and false discovery
rate (FDR) estimation based on the methods proposed in
M. Stephens, 2016, "False discovery rates: a new deal",
<DOI:10.1093/biostatistics/kxw041>. These methods can be applied
whenever two sets of summary statistics---estimated effects and
standard errors---are available, just as 'qvalue' can be applied
to previously computed p-values. Two main interfaces are
provided: ash(), which is more user-friendly; and ash.workhorse(),
which has more options and is geared toward advanced users. The
ash() and ash.workhorse() also provides a flexible modeling
interface that can accommodate a variety of likelihoods (e.g.,
normal, Poisson) and mixture priors (e.g., uniform, normal).

Peter Carbonetto

ashr

Methods for Adaptive Shrinkage, using Empirical Bayes

Matthew Stephens

Chaoxing Dai

David Gerard

Mengyin Lu

Lei Sun

Jason Willwerscheid

Nan Xiao

Mazon Zeng

gen_etruncFUN function

<dl><dt>lcdfFUN</dt>
<dd>the log cdfFUN of the error distribution</dd>
<dt>lpdfFUN</dt>
<dd>the log pdfFUN of the error distribution</dd></dl>

Arguments

gen_etruncFUN — gen_etruncFUN

<dl>

<dt>lcdfFUN</dt>
<dd>the log cdfFUN of the error distribution</dd>


<dt>lpdfFUN</dt>
<dd>the log pdfFUN of the error distribution</dd>

</dl>

gen_etruncFUN: gen_etruncFUN

Description

Usage

Arguments